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MEPS Home Medical Expenditure Panel Survey
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MEPS HC-010D: 1996 Hospital Inpatient Stays
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406


Table of Contents

A. Data Use Agreement 
B. Background 
1.0 Household Component
2.0 Medical Provider Component
3.0 Insurance Component 
4.0 Nursing Home Component 
5.0 Survey Management
C. Technical and Programming Information 
1.0 General Information 
2.0 Data File Information 
2.1 Codebook Structure 
2.2 Reserved Codes 
2.3 Codebook Format 
2.4 Variable Naming 
2.4.1 General 
2.4.2 Expenditure and Sources of Payment Variables 
2.5 File 1 Contents 
2.5.1 Survey Administration Variables 
2.5.1.1 Person Identifiers (DUID, PID, DUPERSID 
2.5.1.2Record Identifiers (EVENTIDX, FFID11X) 
2.5.2 Characteristics of Hospital Inpatient Stay Events 
2.5.2.1 Start and End Dates of Event (IPBEGDD-IPENDYR) 
2.5.2.2 Length of Stay (NUMNIGHX, NUMNIGHT) 
2.5.2.3 Preceding ER Visits (EMERROOM, ERHEVIDX) 
2.5.2.4 Other Visit Detail (SPECCOND - VAPLACE) 
2.5.2.5 VA Facility 
2.5.2.6 MPC Data Indicator (MPCDATA) 
2.5.2.7 Mother/Newborn Flag (MBLINK) 
2.5.3 Condition and Procedure Codes (IPICD1X-IPICD4X, IPPRO1X, IPPRO2X) and Clinical Classification Codes (IPCCC1X-IPCCC4X) 
2.5.3.1 Condition Record Count Variable (NUMCOND 
2.5.4 Flat Fee Variables 
2.5.4.1 Definition of Flat Fee Payments 
2.5.4.2 Flat Fee Variable Descriptions 
2.5.4.3 Flat Fee Type (FFIPTYPE) 
2.5.4.4 Total Number of 1996 Events in Group (FFTOT96) 
2.5.4.5 Caveats of Flat Fee Groups 
2.5.6 Expenditure Data 
2.5.6.1 Definition of Expenditures 
2.5.6.2 Data Editing/Imputation Methodologies of Expenditure Variables 
2.6 File 2 Contents: Pre-imputed Expenditure Variables 
3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96) 
3.1 Details on Person Weights Construction 
4.0 Strategies for Estimation 
4.1 Variables with Missing Values 
4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment 
4.3 Estimates of the Number of Persons with Hospital Inpatient Stays 
4.4 Person-Based Ratio Estimates 
4.4.1 Person-Based Ratio Estimates Relative to Persons with Hospital Inpatient Use 
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population 
4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data with the Hospital Inpatient Stays Data File 
4.6 Variance Estimation 
5.0 Merging/Linking MEPS Data Files 
5.1 Linking a Person-Level File to the Hospital Inpatient Stays File 
5.2 Linking the Hospital Inpatient Stays (HC-010D) to the Medical Conditions File (HC-006) and/or the Prescribed Medicines File (HC-010A) 
5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File) 
5.4 Limitations/Caveats of CLNK (the Medical Conditions Link File) 
6.0 Programming Information 
References 
Attachment 1 
D. Codebooks (link to separate file)
E. Variable-Source Crosswalk 

A. Data Use Agreement

Individual identifiers have been removed from the microdata contained in the files on this CD-ROM. Nevertheless, under sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the purpose for which they were supplied; any effort to determine the identity of any reported cases, is prohibited by law.

Therefore in accordance with the above referenced Federal statute, it is understood that:

  1. No one is to use the data in this data set in any way except for statistical reporting and analysis. If the identity of any person or establishment should be discovered inadvertently, then (a) no use will be made of this knowledge, (b) the Director, Office of Management, AHRQ will be advised of this incident, (c) the information that would identify any individual or establishment will be safeguarded or destroyed, as requested by AHRQ, and (d) no one else will be informed of the discovered identity.
  2. No one will attempt to link this data set with individually identifiable records from any data sets other than the Medical Expenditure Panel Survey or the National Health Interview Survey.

By using these data you signify your agreement to comply with the above-stated statutorily based requirements, with the knowledge that deliberately making a false statement in any matter within the jurisdiction of any department or agency of the Federal Government violates 18 U.S.C. 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison.

The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based upon these data.

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B. Background

This documentation describes one in a series of public use files from the Medical Expenditure Panel Survey (MEPS). The survey provides a new and extensive data set on the use of health services and health care in the United States.

MEPS is conducted to provide nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. MEPS also includes a nationally representative survey of nursing homes and their residents. MEPS is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) (formerly the Agency for Health Care Policy and Research (AHCPR)) and the National Center for Health Statistics (NCHS).

MEPS comprises four component surveys: the Household Component (HC), the Medical Provider Component (MPC), the Insurance Component (IC), and the Nursing Home Component (NHC). The HC is the core survey, and it forms the basis for the MPC sample and part of the IC sample. The separate NHC sample supplements the other MEPS components. Together these surveys yield comprehensive data that provide national estimates of the level and distribution of health care use and expenditures, support health services research, and can be used to assess health care policy implications.

MEPS is the third in a series of national probability surveys conducted by AHRQ on the financing and use of medical care in the United States. The National Medical Care Expenditure Survey (NMCES, also known as NMES-1) was conducted in 1977. The National Medical Expenditure Survey (NMES-2) was conducted in 1987. Beginning in 1996, MEPS continues this series with design enhancements and efficiencies that provide a more current data resource to capture the changing dynamics of the health care delivery and insurance system.

The design efficiencies incorporated into MEPS are in accordance with the Department of Health and Human Services (DHHS) Survey Integration Plan of June 1995, which focused on consolidating DHHS surveys, achieving cost efficiencies, reducing respondent burden, and enhancing analytical capacities. To accommodate these goals, new MEPS design features include linkage with the National Health Interview Survey (NHIS), from which the sampling frame for the MEPS HC is drawn, and continuous longitudinal data collection for core survey components. The MEPS HC augments NHIS by selecting a sample of NHIS respondents, collecting additional data on their health care expenditures, and linking these data with additional information collected from the respondents' medical providers, employers, and insurance providers.

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1.0 Household Component

The MEPS HC, a nationally representative survey of the U.S. civilian noninstitutionalized population, collects medical expenditure data at both the person and household levels. The HC collects detailed data on demographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to care, satisfaction with care, health insurance coverage, income, and employment.

The HC uses an overlapping panel design in which data are collected through a preliminary contact followed by a series of five rounds of interviews over a 2½-year period. Using computer-assisted personal interviewing (CAPI) technology, data on medical expenditures and use for two calendar years are collected from each household. This series of data collection rounds is launched each subsequent year on a new sample of households to provide overlapping panels of survey data and, when combined with other ongoing panels, will provide continuous and current estimates of health care expenditures.

The sampling frame for the MEPS HC is drawn from respondents to NHIS, conducted by NCHS. NHIS provides a nationally representative sample of the U.S. civilian noninstitutionalized population, with oversampling of Hispanics and blacks.

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2.0 Medical Provider Component

The MEPS MPC supplements and validates information on medical care events reported in the MEPS HC by contacting medical providers and pharmacies identified by household respondents. The MPC sample includes all hospitals, hospital physicians, home health agencies, and pharmacies reported in the HC. Also included in the MPC are all office-based physicians who:

  • were identified by the household respondent as providing care for HC respondents receiving Medicaid.
  • were selected through a 75-percent sample of HC households receiving care through an HMO (health maintenance organization) or managed care plan.
  • were selected through a 25-percent sample of the remaining HC households.

Data are collected on medical and financial characteristics of medical and pharmacy events reported by HC respondents, including:

  • Diagnoses coded according to ICD-9-CM (9th Revision, International Classification of Diseases) and DSM-IV (Fourth Edition, Diagnostic and Statistical Manual of Mental Disorders).
  • Physician procedure codes classified by CPT-4 (Common Procedure Terminology, Version 4).
  • Inpatient stay codes classified by DRGs (diagnosis groups).
  • Prescriptions coded by national drug code (NDC), medication name, strength, and quantity dispensed.
  • Charges, payments, and the reasons for any difference between charges and payments.

The MPC is conducted through telephone interviews and mailed survey materials. In some instances, providers sent medical and billing records which were abstracted into the survey instruments.

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3.0 Insurance Component

The MEPS IC collects data on health insurance plans obtained through employers, unions, and other sources of private health insurance. Data obtained in the IC include the number and types of private insurance plans offered, benefits associated with these plans, premiums, contributions by employers and employees, eligibility requirements, and employer characteristics.

Establishments participating in the MEPS IC are selected through four sampling frames:

  • A list of employers or other insurance providers identified by MEPS HC respondents who report having private health insurance at the Round 1 interview.
  • A Bureau of the Census list frame of private-sector business establishments.
  • The Census of Governments from Bureau of the Census.
  • An Internal Revenue Service list of the self-employed.

To provide an integrated picture of health insurance, data collected from the first sampling frame (employers and insurance providers) are linked back to data provided by the MEPS HC respondents. Data from the other three sampling frames are collected to provide annual national and State estimates of the supply of private health insurance available to American workers and to evaluate policy issues pertaining to health insurance.

The MEPS IC is an annual survey. Data are collected from the selected organizations through a prescreening telephone interview, a mailed questionnaire, and a telephone followup for nonrespondents.

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4.0 Nursing Home Component

The 1996 MEPS NHC was a survey of nursing homes and persons residing in or admitted to nursing homes at any time during calendar year 1996. The NHC gathered information on the demographic characteristics, residence history, health and functional status, use of services, use of prescription medicines, and health care expenditures of nursing home residents. Nursing home administrators and designated staff also provided information on facility size, ownership, certification status, services provided, revenues and expenses, and other facility characteristics. Data on the income, assets, family relationships, and care-giving services for sampled nursing home residents were obtained from next-of-kin or other knowledgeable persons in the community.

The 1996 MEPS NHC sample was selected using a two-stage stratified probability design. In the first stage, facilities were selected; in the second stage, facility residents were sampled, selecting both persons in residence on January 1, 1996, and those admitted during the period January 1 through December 31.

The sample frame for facilities was derived from the National Health Provider Inventory, which is updated periodically by NCHS. The MEPS NHC data were collected in person in three rounds of data collection over a 1½-year period using the CAPI system. Community data were collected by telephone using computer-assisted telephone interviewing (CATI) technology. At the end of three rounds of data collection, the sample consisted of 815 responding facilities, 3,209 residents in the facility on January 1, and 2,690 eligible residents admitted during 1996.

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5.0 Survey Management

MEPS data are collected under the authority of the Public Health Service Act. They are edited and published in accordance with the confidentiality provisions of this act and the Privacy Act. NCHS provides consultation and technical assistance.

As soon as data collection and editing are completed, the MEPS survey data are released to the public in staged releases of summary reports and microdata files. Summary reports are released as printed documents and electronic files. Microdata files are released on CD-ROM and/or as electronic files.

Printed documents and CD-ROMs are available through the AHRQ Publications Clearinghouse. Write or call:

AHRQ Publications Clearinghouse
Attn: (publication number)
P.O. Box 8547
Silver Spring, MD 20907
800/358-9295
410/381-3150 (callers outside the United States only)
888/586-6340 (toll-free TDD service; hearing impaired only)

Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. Selected electronic files are available from the Internet on the MEPS web site: <http://www.meps.ahrq.gov/>.

Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Cost and Financing Studies, Agency for Healthcare Research and Quality.

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C. Technical and Programming Information

1.0 General Information

This documentation describes one in a series of public use event files from the 1996 Medical Expenditure Panel Survey Household (HC) and Medical Provider Components(MPC) . Released as an ASCII data file and SAS transport file, this public use file provides detailed information on hospital inpatient stays for a nationally representative sample of the civilian noninstitutionalized population of the United States and can be used to make estimates of hospital inpatient stay utilization and expenditures for calendar year 1996. Each record on this event file represents a unique hospital inpatient stay event; that is, a hospital inpatient stay reported by the household respondent. In addition to expenditures related to the stay, each record contains household reported medical conditions and procedures associated with the hospitalization and information on the length of stay.

Data from this event file can be merged with other 1996 MEPS HC data files for purposes of appending person characteristics such as demographic or health insurance coverage to each hospital inpatient stay record.

Counts of hospital inpatient stay utilization are based entirely on household reports. Information from the MEPS MPC was used to supplement expenditure and payment data reported by the household.

This file can be also used to construct summary variables of expenditures, sources of payment, and related aspects of hospital inpatient care. Aggregate annual person-level information on the use of hospital inpatient stays and other health services use is provided on public use file HC-011, where each record represents a MEPS sampled person.

The following documentation offers a brief overview of the types and levels of data provided, the content and structure of the files and the codebook, and programming information. It contains the following sections:

Data File Information

Sample Weights and Variance Estimation Variables

Merging MEPS Data Files

Programming Information

References

Codebook

Variable to Source Crosswalk

For more information on MEPS HC survey design see S. Cohen, 1997; J. Cohen, 1997; and S. Cohen, 1996. For information on the MEPS MPC design, see S. Cohen, 1998. A copy of the survey instruments used to collect the information on this file is available on the MEPS web site at the following address: <http://www.meps.ahrq.gov>.

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2.0 Data File Information

This public use data set consists of 2 event-level data files. File 1 contains characteristics associated with the hospital inpatient stay event and imputed expenditure data. File 2 contains pre-imputed and unimputed expenditure data from both the Household and Medical Provider Components for all hospital inpatient stay events on File 1. Please see the Attachment 1 for definitions of imputed, un-imputed and pre-imputed expenditure variables.

Both files 1 and 2 of this public use data set contains 2,207 hospital inpatient stay records. Of the 2,207 hospital inpatient stay records, 2,138 are associated with persons having a positive person-level weight (WTDPER96). These files include hospital inpatient stay records for all household survey respondents who resided in eligible responding households and reported at least one hospital inpatient stay. Each record represents one household-reported hospital inpatient stay that occurred during calendar year 1996. Hospital inpatient stays known to have occurred after December 31, 1996 are not included on this file. Some household respondents may have multiple hospital inpatient stays and thus will be represented in multiple records on this file. Other household respondents may have reported no hospital inpatient stays and thus will have no records on this file. These data were collected during rounds 1, 2, and 3 of the MEPS HC. The persons represented on this file had to meet the following three criteria:

1) The hospital stay had to have been reported by a household survey respondent as an inpatient hospital stay (regardless of a stay's length). Thus, the file contains some hospitalizations that were reported as not including an overnight stay.

2) The hospital stay had to have ended during 1996. Stays that began prior to 1996, but ended during 1996, are included on this file. Stays that began in 1996, but ended during 1997, are excluded from this file and will be represented on a subsequent 1997 data file. Please note that persons with no hospital inpatient stays use for 1996 are not included on this file (but are represented on MEPS person-level files).

3) The persons represented on this file had to also meet either 3a or 3b:

a) Be classified as a key in-scope person who responded for his or her entire period of 1996 eligibility (i.e., persons with a positive 1996 full-year person-level sampling weight (WTDPER96 > 0)), or

b) Be classified as either an eligible non-key person or an eligible out-of-scope person who responded for his or her entire period of 1996 eligibility, and belonged to a family (i.e., all persons within a household (DUID) with the same value of FAMID) in which all eligible family members responded for their entire period of 1996 eligibility, and at least one family member has a positive 1996 full-year person weight (i.e., eligible non-key or eligible out-of-scope persons who are members of a family all of whose members have a positive 1996 full-year family-level weight (WTFAM96 >0)).

Please refer to Attachment 1 for definitions of key, non-key, inscope and eligible.

One caveat that should be noted is that in the case of a newborn, and the inpatient hospital stay associated with the newborn's birth, a separate hospital inpatient stay record exists on the file only if the newborn was discharged after the mother. Thus, hospital stays associated with a normal birth are generally represented on the file as a single record (i.e., the mother's hospital inpatient stay record, covering expenditure data for both the mother and baby). In situations where the newborn was discharged after the mother, the birth event will be represented as two records (one record for the mother and one record for the baby). For newborns re-admitted to the hospital during the reference year, each subsequent re-admission will have a separate record.

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Each hospital inpatient stay record on File 1 includes the following: start and end dates of the hospital inpatient stay; number of nights in the hospital; reason entered the hospital; main surgical procedure; condition(s) associated with the hospital inpatient stay; medicines prescribed at discharge ; flat fee information, imputed sources of payment, total payment and total charge for both the facility and physician components of the hospital inpatient stay expenditure; and a full-year person-level weight.

File 2 of this public use data set is intended for analysts who want to perform their own imputations to handle missing data. . This file contains one set of un-imputed expenditure information from the Medical Provider Component as well as one set of pre-imputed expenditure information from the Household Component. Both sets of expenditure data have been subject to minimal logical editing that accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out of pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMO's and private HMO's as payment sources. However, missing data was not imputed.

Data from these files can be merged with previously released 1996 MEPS HC person level data using the unique person identifier, DUPERSID, to append person characteristics such as demographic or health insurance characteristics to each record. Hospital inpatient stay events can also be linked to the MEPS 1996 Medical Conditions File (HC-006) and the MEPS 1996 Prescribed Medicines File (HC-10A). The Appendix File contains details on how to link MEPS data files.

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2.1 Codebook Structure

For each variable on these files, both weighted and unweighted frequencies are provided. The codebook and data file sequence list variables in the following order:

File 1

Unique person identifiers

Unique hospital inpatient stay identifiers

Other survey administration variables

Hospital inpatient stay characteristics variables

ICD-9 codes

Clinical Classification Software codes

Imputed expenditure variables

Weight and variance estimation variables

File 2

Unique person identifiers

Unique hospital inpatient stay identifiers

Pre-imputed expenditure variables

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2.2 Reserved Codes

The following reserved code values are used:

VALUE DEFINITION

-1 INAPPLICABLE Question was not asked due to skip pattern.

-2 DETERMINED IN A PREVIOUS ROUND

-3 NO DATA IN ROUND

-5 NEVER WILL KNOW

-6 INAPPLICABLE Not asked due to person being under age 5

-7 REFUSED Question was asked and respondent refused to answer question.

-8 DK Question was asked and respondent did not know answer.

-9 NOT ASCERTAINED Interviewer did not record the data.

-10 HOURLY WAGE VALUE SUPPRESSED Hourly Wage was suppressed.

-13 VALUE SUPPRESSED Value Suppressed

Generally, the values of -1,-7, -8, and -9 have not been edited on this file. The values of -1 and -9 can be edited by analysts by following the skip patterns in the questionnaire.

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2.3 Codebook Format

This codebook describes an ASCII data set (although the data are also being provided in a SAS transport file). The following codebook items are provided for each variable:

IDENTIFIER  DESCRIPTION
Name  Variable name (maximum of 8 characters)
Description  Variable descriptor (maximum 40 characters)
Format  Number of bytes
Type  Type of data: numeric (indicated by NUM) or character (indicated by CHAR)
Start  Beginning column position of variable in record
End  Ending column position of variable in record

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2.4 Variable Naming

In general, variable names reflect the content of the variable, with an 8 character limitation.

For questions asked in a specific round, the end digit in the variable name reflects the round in which the question was asked. All imputed/edited variables end with an "X".

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2.4.1 General

Variables contained on Files 1, 2 and 3 were derived either from the HC questionnaire itself, the MPC data collection instrument or from the CAPI. The source of each variable is identified in Appendix 1, entitled, "Variable to Source Crosswalk". Sources for each variable are indicated in one of three ways: (1) variables which are derived from CAPI or assigned in sampling are so indicated; (2) variables which come from one or more specific questions have those numbers and the questionnaire section indicated in the "Source" column; (3) variables constructed from multiple questions using complex algorithms are labeled "Constructed" in the "Source" column and (4) variables which have been imputed are so indicated..

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2.4.2 Expenditure and Sources of Payment Variables

Both pre-imputed and imputed versions of the expenditure and sources of payment variables are provided on 2 separate files. Expenditure variables on Files 1 and 2 of the MEPS event files follow a standard naming convention and are 8 characters in length. Please note that pre-imputed means that a series of logical edits have been performed on the variable but missing data remains. The imputed versions incorporate the same edits but have also undergone an imputation process to account for missing data.

The pre-imputed expenditure variables on File 2 end with an "H", if the data source was from the MEPS Household Component and ends with a "M" if the data source was the MEPS Medical Provider Component. All imputed variables on File 1 end with an "X".

The total sum of payments, 12 sources of payment variables and total charge variables are named consistently in the following way:

The first two characters indicate the type of event:

IP - inpatient stay

OB - office-based visit

ER - emergency room visit

OP - outpatient visit

HH - home health visit 

DV - dental visit

OM - other medical equipment 

RX - prescribed medicine

For expenditure variables on these files, the third character indicates whether the expenditure is associated with the facility (F) or the physician (P).

In the case of the source of payment variables, the fourth and fifth characters indicate:

SF - self or family 

OF - other Federal Government 

XP - sum of payments

MR - Medicare 

SL - State/local government

MD - Medicaid 

WC - Worker's Compensation

PV - private insurance

OT - other insurance

VA - Veterans

OR - other private

CH - CHAMPUS/CHAMPVA

OU - other public

The sixth and seventh characters indicate the year (96). The eighth character of all imputed/edited variables is an " X".

For example, IPFSF96X is the edited/imputed amount paid by self or family for the facility portion of the hospital inpatient stay expenditure incurred in 1996.

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2.5 File 1 Contents

2.5.1 Survey Administration Variables

2.5.1.1 Person Identifiers (DUID, PID, DUPERSID)

The dwelling unit ID (DUID) is a 5-digit random number assigned after the case was sampled for MEPS. The 3-digit person number (PID) uniquely identifies each person within the dwelling unit. The 8-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. For detailed information on dwelling units and families, please refer to the documentation on public use file HC-008.

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2.5.1.2 Record Identifiers (EVENTIDX, FFID11X)

EVENTIDX uniquely identifies each event/stay (i.e. each record on the file) and is the variable required to link hospital inpatient stay events to data files containing details on conditions and/or prescribed medicines (HC-006 and H-010A, respectively). For details on linking see Section 5.0.

FFID11X uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment situation. For example, dialysis treatments are typically covered in a flat fee arrangement where all visits are covered under one flat fee dollar amount. These events have the same value for FFID11X. Please note that FFID11X should be used to link up all MEPS event files (excluding prescribed medicines) in order to determine the full set of events that are part of a flat fee group.

EVENTRN indicates the round in which the hospital inpatient stay was first reported.

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2.5.2 Characteristics of Hospital Inpatient Stay Events

File 1 contains 86 variables describing hospital inpatient stays reported by respondents in the Hospital Stays section of the MEPS HC questionnaire. The questionnaire contains probes for determining specific details about the hospital inpatient stay. Unless noted otherwise, the following variables are provided as unedited.

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2.5.2.1 Start and End Dates of Event (IPBEGDD-IPENDYR)

File 1 contains variables describing hospital inpatient stays reported by household respondents in the Hospital Section of the MEPS HC questionnaire. There are three variables which indicate the day, month and year a hospital stay began (IPBEGDD, IPBEGMM, IPBEGYR, respectively). Similarly, there are three variables which indicate the day, month and year a hospital stay ended (IPENDDD, IPENDMM, IPENDYR, respectively). These variables have not been edited.

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2.5.2.2 Length of Stay (NUMNIGHX, NUMNIGHT)

The edited variable NUMNIGHX denotes length of hospital stay. For stays beginning in 1995 and ending in 1996, this variable would include the nights associated with 1995. It was edited using the above mentioned begin and end dates of the hospital inpatient stay. If the dates were unknown, then the unedited variable NUMNIGHT(number of nights in the hospital) was used. Users should note that NUMNIGHT was only asked for events with missing date information. Hence, it contains large amounts of missing data and cannot be used alone but rather in conjunction with date information.

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2.5.2.3 Preceding ER Visits (EMERROOM, ERHEVIDX)

The variable EMERROOM was derived directly from the Hospital Stays section of the HC survey instrument and is provided as unedited. EMERROOM describes whether the hospital inpatient stay began with an emergency room visit. Users should be aware that no attempt was made to reconcile EMERROOM with information from the Emergency Room Visit File. Discrepancies do exist where the hospital stays record indicates that there is a preceding emergency room visit but no such visit exists on the Emergency Room File.

The variable ERHEVIDX is a constructed variable which identifies hospital stays whose expenditures include the expenditures for the preceding emergency room visit. This variable was constructed by comparing date information for the reported hospital stay and all emergency room visits for the same person. ERHEVIDX has not been reconciled with the unedited variable EMERROOM.

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2.5.2.4 Other Visit Detail (SPECCOND - VAPLACE)

Also provided are the following unedited variables: hospital inpatient stay related to condition (SPECCOND), reason entered hospital (RSNINHOS), any operation or surgery performed while respondent was in hospital (ANYOPER) and if surgery performed then what was the main surgical procedure (SURGPROC), any medicine prescribed at discharge (DSCHPMED), and finally, any physician seen outside the hospital (DROUTSID).

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2.5.2.5 VA Facility

VAPLACE is a constructed variable that indicates whether the provider worked at a VA facility. This only has valid data for providers that were sampled into the Medical Provider Component. All other providers are classified as unknown.

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2.5.2.6 MPC Data Indicator (MPCDATA)

While all hospital inpatient events are sampled into the Medical Provider Component, not all hospital inpatient stay records have MPC data associated with them. This is dependent upon the cooperation of the household respondent to provide permission forms to contact the hospital as well as the cooperation of the hospital to participate in the survey. MPCDATA indicates whether or not MPC data was collected for the hospital inpatient stay.

2.5.2.7 Mother/Newborn Flag (MBLINK)

The variable MBLINK flags hospital stays where expenditures for the delivery of a newborn are included in the mother's record. See Section 2.5.6.2 for details on mother/newborn expenditures .

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2.5.3 Condition and Procedure Codes (IPICD1X-IPICD4X, IPPRO1X, IPPRO2X) and Clinical Classification Codes (IPCCC1X-IPCCC4X)

Information on household reported medical conditions and procedures associated with each hospital inpatient stay event are provided on this file. There are up to four condition codes (IPICD1X-IPICD4X) and up to two procedure codes (IPPRO1X and IPPRO2X) listed for each hospital inpatient stay event (99.5% of hospital inpatient stay events have 0-3 condition records linked). In order to obtain complete condition information associated with an event, the analyst must link to the HC-006 Medical Conditions File. Details on how to link this file to the MEPS Medical Conditions File (HC-006) are provided in Section 5.0. The user should note that due to confidentiality restrictions, provider reported condition information are not publicly available.

The medical conditions and procedures reported by the Household Component respondent were recorded by the interviewer as verbatim text, which were then coded to fully-specified 1996 ICD-9-CM codes, including medical condition and V codes (see Health Care Financing Administration, 1980), by professional coders. Although codes were verified and error rates did not exceed 2.5 percent for any coder, analysts should not presume this level of precision in the data; the ability of household respondents to report condition data that can be coded accurately should not be assumed (see Cox and Cohen, 1985; Cox and Iachan, 1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993). For detailed information on conditions, please refer to the documentation on HC-006 1996 Medical Condition File. For frequencies of conditions by event type, please see the Appendix File.

The ICD-9-CM condition and procedure codes were aggregated into clinically meaningful categories. These categories, included on the file as IPCCC1X-IPCCC4X, were generated using Clinical Classification Software (formerly known as Clinical Classifications for Health Care Policy Research (CCHPR)), (Elixhauser, et al., 1998), which aggregates conditions and V-codes into 260 mutually exclusive categories, most of which are clinically homogeneous.

In order to preserve respondent confidentiality, nearly all of the condition codes provided on this file have been collapsed from fully-specified codes to 3-digit code categories. The reported ICD-9-CM code values were mapped to the appropriate clinical classification category prior to being collapsed to the 3-digit categories.

The condition and procedure codes (and clinical classification codes) linked to each hospital inpatient stay event are sequenced in the order in which the conditions were reported by the household respondent, which was in chronological order of occurrence and not in order of importance or severity. Analysts who use the HC-006 Medical Conditions file in conjunction with this hospital inpatient stay event file should note that the order of conditions on this file is not identical to that on the Medical Conditions file.

The user should note that due to the design of the HC survey instrument, most hospital stays that are reported as being for a delivery (RSNINHOS=4) link to condition codes that are for pregnancy rather than a delivery. In addition, RSNINHOS has not been reconciled with the ICD-9 condition codes, procedure codes nor CCC codes that are on the file.

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2.5.3.1 Condition Record Count Variable (NUMCOND)

The variable NUMCOND indicates the total number of condition records which can be linked from HC-006: Medical Conditions File to each hospital inpatient stay event. For events where no condition records linked (NUMCOND=0), the condition and procedure and clinical classification code variables all have a value of -1 INAPPLICABLE. Similarly, for events without a linked second or third condition record, the corresponding second or third condition and procedure and clinical classification code variable was set to -1 INAPPLICABLE.

In order to obtain complete condition information for events with NUMCOND greater than 3, the analyst must link to the MEPS Condition File (HC-006). Please see Section 5.0 for details on linking MEPS data files.

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2.5.4 Flat Fee Variables

2.5.4.1 Definition of Flat Fee Payments

A flat fee is the fixed dollar amount a person is charged for a package of health care services. Examples would be: obstetrician's fee covering a normal delivery, as well as pre- and post-natal care; or a surgeon's fee covering surgical procedure and post-surgical care. A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee payment situation. The flat fee groups represented on this file (and all of the other 1996 MEPS event files), include flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 1996. By definition a flat fee group can span multiple years and/or event types (e.g., hospital stay, physician office visit), and a single person can have multiple flat fee groups.

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2.5.4.2 Flat Fee Variable Descriptions

There are several variables on this file that describe a flat fee payment situation and the number of medical events that are part of a flat fee group. As noted previously, for a person, the variable FFID11X can be used to identify all events, that are part of the same flat fee group. To identify such events, FFID11X should be used to link events from all MEPS event files (excluding prescribed medicines HC-010A). For the hospital stays that are not part of a flat fee payment situation, the flat fee variables described below are all set to inapplicable (-1).

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2.5.4.3 Flat Fee Type (FFIPTYPE)

FFIPTYPE indicates whether the 1996 hospital stay is the "stem" or "leaf" of a flat fee group. A stem (records with FFIPTYPE = 1) is the initial medical service (event) which is followed by other medical events that are covered under the same flat fee payment. The leaf of the flat fee group (records with FFIPTYPE = 2) are those medical events that are tied back to the initial medical event (the stem) in the flat fee group. These "leaf" records have their expenditure variables set to zero.

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2.5.4.4 Total Number of 1996 Events in Group (FFTOT96)

If a hospital stay is part of a flat fee group, the variable FFTOT96 counts the total number of all known events (that occurred during 1996) covered under a single flat fee payment situation. This count includes the hospital stay record in the count.

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2.5.4.5 Caveats of Flat Fee Groups

The user should note that flat fee payment situations are not common with respect to hospital inpatient stays . Hence, there are only 18 hospital inpatient stay events that are identified as being part of a flat fee payment group. In order to correctly identify all events that are part of a flat fee group, the user should link all MEPS event files (excluding the prescribed medicine file: HC-010A) using the variable FFID11X.

In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit (leaf). There are some situations where this is not true. For some of these flat fee groups, the initial visit reported occurred in 1996 but the remaining visits that were part of this flat fee group occurred in 1997. In this case, the 1996 flat fee group would consist of one event (the stem). The 1997 events that are part of this flat fee group are not represented on the file. Similarly, the household respondent may have reported a flat fee group where the initial visit began in 1995 but subsequent visits occurred during 1996. In this case, the initial visit would not be represented on the file. This 1996 flat fee group would then only consist of one or more leaf records and no stem. Another reason for which a flat fee group would not have a stem and a leaf record is that the stems or leaves could have been reported as different event types. In a small number of cases, there are flat fee bundles that span various event types. The stem may have been reported as one event type and the leaves may have been reported as another event type. In order to determine this, the analyst must link all event files (excluding the prescribed medicine file: HC-010A) using the variable FFID11X to create the flat fee group.

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2.5.6 Expenditure Data

2.5.6.1 Definition of Expenditures

Expenditures on this file refer to what is paid for health care services. More specifically, expenditures in MEPS are defined as the sum of payments for care received for each hospital stay, including out of pocket payments and payments made by private insurance, Medicaid, Medicare and other sources. The definition of expenditures used in MEPS differs slightly from its predecessors: the 1987 NMES and 1977 NMCES surveys where "charges" rather than sum of payments were used to measure expenditures. This change was adopted because charges became a less appropriate proxy for medical expenditures during the 1990's due to the increasingly common practice of discounting. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, these estimates do not incorporate any payment not directly tied to specific medical care visits, such as bonuses or retrospective payment adjustments paid by third party payers. Another general change from the two prior surveys is that charges associated with uncollected liability, bad debt, and charitable care (unless provided by a public clinic or hospital) are not counted as expenditures because there are no payments associated with those classifications. While charge data is provided on this file, analysts should use caution when working with this data because a charge does not typically represent actual dollars exchanged for services or the resource costs of those services, nor are they directly comparable to the expenditures defined in the 1987 NMES. For details on expenditure definitions, please reference the following, "Informing American Health Care Policy" (Monheit, Wilson, Arnett, 1999).

Expenditure data related to hospital events are broken out by facility and separately billing doctor expenditures. This file contains five categories of expenditure variables per stay: basic hospital facility expenses; expenses for doctors who billed separately from the hospital for any inpatient services provided during hospital stay; total expenses, which is the sum of the facility and physician expenses; facility total charge and physician total charge.

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2.5.6.2 Data Editing/Imputation Methodologies of Expenditure Variables

General Imputation Methodology

The expenditure data included on this file were derived from both the MEPS Household (HC) and Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers was used in the expenditure imputation process to supplement missing household data. For all hospital inpatient stays, MPC data were used if complete; otherwise HC data were used if complete. Missing data for hospital inpatient stays where HC data were not complete and MPC data were not collected or complete were derived through the imputation process.

Logical edits were used to resolve internal inconsistencies and other problems in the HC and MPC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out of pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMO's and private HMO's as payment sources. These edits produced a complete vector of expenditures for some events, and provided the starting point for imputing missing expenditures in the remaining events.

A weighted sequential hot-deck procedure was used to impute for missing expenditures as well as total charge. The procedure uses survey data from respondents to replace missing data, while taking into account the respondents' weighted distribution in the imputation process. Classification variables vary by event type in the hot-deck imputations, but total charge and insurance coverage are key variables in all of the imputations. Separate imputations were performed for nine categories of medical provider care: inpatient hospital stays, outpatient hospital department visits, emergency room visits, visits to physicians, visits to non-physician providers, dental services, home health care by certified providers, home health care by paid independents, and other medical expenses. After the imputations were finished, visits to physician and non-physician providers were combined into a single medical provider file. The two categories of home care also were combined into a single home health file.

Expenditures for services provided by separately billing doctors in hospital settings were also edited and imputed. These expenditures are shown separately from hospital facility charges for hospital inpatient, outpatient, and emergency room care.

Capitation Imputation

The imputation process was also used to make expenditure estimates at the event level for events that were paid on a capitated basis. The capitation imputation procedure was designed as reasonable approach to complete event level expenditures for respondents in managed care plans. This procedure was conducted in two stages. First, HMO events reported in the MPC as covered by capitation arrangements were imputed using similar HMO events paid on a fee-for-service, with total charge as a key variable. Then this completed set of MPC events was used as the donor pool for unmatched household-reported events for sample persons in HMOs. By using this strategy, capitated HMO events were imputed as if the provider were reimbursed from the HMO on a discounted fee-for-service basis.

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Imputation Methodology for Hospital Inpatient Stays

Facility expenditures for inpatient hospital stays were developed in a sequence of logical edits and imputations. "Household" edits were applied to sources and amounts of payment for all events reported by HC respondents. "MPC" edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events. Both sets of edits were used to correct obvious errors (as described above) in the reporting of expenditures. After the data from each source were edited, a decision was made as to whether household- or MPC-reported information would be used in the final editing and hot-deck imputations for missing expenditures. The general rule was that MPC data would be used for events where a household reported event corresponded to a MPC reported event (i.e. a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households.

Separate imputations were performed for flat fee and simple events. Most inpatient hospital stays were imputed as simple events because facility charges for an inpatient hospital stay are rarely grouped with other events. (See Section 2.5.4 for details on flat fee groups.)

Logical edits also were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the hot-deck imputations, while events with missing expenditure data were assigned to various recipient categories. Each event was assigned to a recipient category based on its pattern of missing data. For example, an event with a known total charge but no expenditures information were assigned to one category, while an event with a known total charge and some expenditures information was assigned to a different category. Similarly, events without a known total charge were assigned to various recipient categories based on the amount of missing data.

The logical edits produced eight recipient categories in which all events had a common pattern of missing data. Separate hot-deck imputations were performed on events in each recipient category, and the donor pool was restricted to events with complete expenditures from the MPC. The donor pool restriction was used even though some unmatched events had complete household-reported expenditures. These events were not allowed to donate information to other events because the MPC data were considered to be more reliable.

The donor pool included "free events" because, in some instances, providers are not paid for their services. These events represent charity care, bad debt, provider failure to bill, and third party payer restrictions on reimbursement in certain circumstances. If free events were excluded from the donor pool, total expenditures would be over-counted because the cost of free care would be both implicitly included in paid events, and explicitly included in events that should have been treated as free from provider.

Flat Fee Expenditures

The approach used to count expenditures for flat fees was to place the expenditure on the first visit of the flat fee group. The remaining visits have zero payments. Thus, if the first visit in the flat fee group occurred prior to 1996, all of the events that occurred in 1996 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 1996, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 1996. See section 2.5.4 for details on the flat fee variables.

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Zero Expenditures

There are some medical events reported by respondents where the payments were zero. This could occur for several reasons including (1) free care was provided, (2) bad debt was incurred, (3) care was covered under a flat fee arrangement beginning in an earlier year, or (4) follow-up visits were provided without a separate charge (e.g., after a surgical procedure). If all of the medical events for a person fell into one of these categories, then the total annual expenditures for that person would be zero.

Discount Adjustment Factor

An adjustment was also applied to some HC reported expenditure data because an evaluation of matched HC/MPC data showed that respondents who reported that charges and payments were equal were often unaware that insurance payments for the care had been based on a discounted charge. To compensate for this systematic reporting error, a weighted sequential hot-deck imputation procedure was implemented to determine an adjustment factor for HC reported insurance payments when charges and payments were reported to be equal. As for the other imputations, selected predictor variables were used to form groups of donor and recipient events for the imputation process.

Mother/Newborn Expenditures

Expenditure data for newborns were edited to exclude discharges after birth when the newborn left the hospital on the same day as the mother. As a result, inpatient expenditures reported for 1996 births were usually applied to the mother and not treated as separate expenditures for the infant. However, if a newborn was discharged at a later date than the mother, then the hospitalization was treated as a separate hospital stay for the newborn.

This means that in most cases, expenditure data for the newborn is included on the mother's record. A separate record for the newborn only exists if the newborn was discharged after the mother. In this case, the expenditure for the newborn is on the newborn's record.

In addition, the user should note that for the purposes of the expenditure imputation, deliveries were identified using the variable RSINHOS which has not been reconciled with pregnancy and delivery ICD-9 codes on this file as well as on HC-006. As mentioned previously, in most instances where RSINHOS= 4 delivery, the ICD-9 code indicates a pregnancy rather than a delivery.

Hospital/Emergency Room Expenditures

Although a person may have indicated that there was an emergency room visit that preceded this hospital stay (EMEROOM), there was no verification that, in fact, the emergency room visit was actually recorded within the Emergency Room Section of the questionnaire.

While it is true that all of the event files can be linked by DUPERSID, there is no unique record link between inpatient stays and emergency room visits. That is, a person could have one inpatient stay and three emergency room visits during the calendar year. While the inpatient stay record may indicate that it was preceded by an emergency room visit, there is no unique record link to the appropriate (of the three) emergency room visit. However, where this relationship could be identified (using MPC start and end date of the events as well as information from the provider), the expenditure associated with the emergency room visit was moved to the hospital facility expenditure (see Section 2.5.2.3). Hence, for some hospital stays, expenditures for a preceding emergency room visits are included. In these situations, the corresponding emergency room record on HC-010E: Emergency Room Visit File will have its expenditure information zeroed out to avoid double-counting. The variable ERHEVIDX identifies these hospital stays whose expenditures include the expenditures for the preceding emergency room visit. It should also be noted that for these cases, there is only one hospital stay associated with the emergency room stay.

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Sources of Payment

In addition to total expenditures, variables are provided which itemize expenditures according to major sources of payment categories. These categories are:

1. Out of pocket by user or family

2. Medicare

3. Medicaid

4. Private Insurance

5. Veteran's Administration, excluding CHAMPVA

6. CHAMPUS or CHAMPVA

7. Other Federal sources - includes Indian Health Service, Military Treatment Facilities, and other care by the Federal government

8. Other State and Local Source - includes community and neighborhood clinics, State and local health departments, and State programs other than Medicaid.

9. Worker's Compensation

10. Other Unclassified Sources - includes sources such as automobile, homeowner's, liability, and other miscellaneous or unknown sources.

Two additional source of payment variables were created to classify payments for events with apparent inconsistencies between insurance coverage and sources of payment based on data collected in the survey. These variables include:

11. Other Private - any type of private insurance payments reported for persons not reported to have any private health insurance coverage during the year as defined in MEPS; and

12. Other Public - Medicaid payments reported for persons who were not reported to be enrolled in the Medicaid program at any time during the year.

Though relatively small in magnitude, users should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and source of payment questions in the survey, some of these inconsistencies may have logical explanations. For example, private insurance coverage in MEPS is defined as having a major medical plan covering hospital and physician services. If a MEPS sampled person did not have such coverage but had a single service type insurance plan (e.g. dental insurance) that paid for a particular episode of care, those payments may be classified as "other private". Some of the "other public" payments may stem from confusion between Medicaid and other state and local programs or may be from persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the program.

Users should also note that the Other Public and Other private source of payment categories only exist on File 1 for imputed expenditure data since they were created through the editing/imputation process. File 2 reflects 10 sources of payment as they were collected through the survey instrument.

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Imputed Hospital Inpatient Stay Expenditure Variables

This file contains 2 sets of imputed expenditure variables: facility expenditures and physician expenditures.

Hospital Inpatient Facility Expenditures (IPFSF96X-IPFOT96X, IPFTC96X, IPFXP96X)

Hospital facility expenses include all expenses for direct hospital care, including room and board, diagnostic and laboratory work, x-rays, and similar charges, as well as any physician services included in the hospital charge.

Hospital facility expenditures were obtained primarily through the MPC. If the physician charges were included in the hospital bill, then this expenditure is included in the facility expenditure variables. The imputed facility expenditures are provided on this file. IPFSF96X - IPFOT96X are the 12 sources of payment, IPFTC96X is the total charge, and IPFXP96X is the sum of the 12 sources of payments for the facility expenditure. The 12 source of payment categories are: self/family, Medicare, Medicaid, private insurance, Veterans Administration, CHAMPUS/CHAMPVA, other federal, state/local governments, Workman's Compensation, other private insurance, other public insurance and other insurance.

Hospital Inpatient Physician Expenditures (IPDSF96X - IPDOT96X, IPDTC96X, IPDXP96X)

Separately billing doctor (SBD) expenses typically cover services provided to patients in hospital settings by providers like anesthesiologists, radiologists, and pathologists, whose charges are often not included in hospital bills.

For medical doctors who bill separately (i.e. outside the hospital bill), a separate data collection effort within the Medical Provider Component was performed to obtain this same set of expenditure information from each separately billing doctor. It should be noted that there could be several separately billing doctors associated with a medical event. For example, a hospital inpatient stay could have a radiologist, anesthesiologist, pathologist and a surgeon associated with it. If their services are not included in the hospital bill then this is one medical event with 4 separately billing doctors. The imputed expenditure information associated with the separately billing doctors for a hospital inpatient stay (i.e. the expenditures incurred by the radiologist + anesthesiologist + pathologist + surgeon) and is provided on the file. IPDSF96X - IPDOT96X are the 12 sources of payment, IPDTC96X is the total charge, and IPDXP96X is the sum of the 12 sources of payments.

Analysts need to take into consideration whether to analyze facility and SBD expenditures separately, combine them within service categories, or collapse them across service categories (e.g. combine SBD expenditures with expenditures for physician visits to offices and/or outpatient departments). Analysts interested in total expenditure should use the variable IPEXP96X, which includes both the facility and physician amounts.

Rounding

Expenditure variables on File 1 have been rounded to the nearest penny. Person level expenditure information released on HC-011 were rounded to the nearest dollar. It should be noted that using the MEPS event files HC-010A through HC-010H to create person level totals will yield slightly different totals than that found on HC-011. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the event files (HC-010A-HC-010H) for a particular source of payment may differ from the number of persons with expenditures on the person level expenditure file (HC-011) for that source of payment This difference is also an artifact of rounding only. Please see the Appendix File for details on such rounding differences.

Imputation Flags (IMPIPFSF - IMPIPCHG)

The variables IMPIPFSF - IMPIPCHG identify records where sources of payment and total charge for the facility portion of the expenditure have been imputed using the methodologies outlined in this document. The variable IMPIPNUM indicates the number of physician records associated with the hospital stay where the physician portion of the expenditures have been imputed. It is not available for individual sources of payment.

When a record was identified as being the leaf of a flat fee group, the values of all imputation flags were set to "0" (not imputed) since they were not included in the imputation process.

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2.6 File 2 Contents: Pre-imputed Expenditure Variables

Both imputed and pre-imputed expenditure data are provided on this file. Pre-imputed means that only a series of logical edits were applied to both the HC and MPC data to correct for several problems including outliers, copayments or charges reported as total payments, and reimbursed amounts counted as out of pocket payments. Edits were also implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMO's and private HMO's as payment sources as well as a number of other data inconsistencies that could be resolved through logical edits. Missing data were not imputed.

As described previously, there are essentially two components that went into creating the total medical expenditure variable: household reported expenditure data and provider reported expenditure data. Both sets of expenditure data are provided in their pre-imputed form and have not gone through the same level of quality control as their imputed counterpart. This means that (in some instances) there are large amounts of missing data. The household and provider reported facility pre-imputed expenditure data are provided on this file (IPSF96H - IPOT96H and IPFSF96M-IPFOT96M respectively).

The user shall note that there exist only 10 sources of payment variables in the pre-imputed expenditure data, while the imputed expenditure data on File 1 contains 12 sources of payment variables. The additional two sources of payment (which are not reported as separate sources of payment through the data collection) are Other Private and Other Public. These sources of payment categories were constructed to resolve apparent inconsistencies between individuals' reported insurance coverage and their sources of payment for specific events..

The user should also note that the variable HHSFFIDX is the original flat fee identifier that was derived during the household interview. This identifier should only be used if the analyst is interested in performing their own expenditure imputation.

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3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96)

Overview

There is a single full year person-level weight (WTDPER96) included on this file. A person-level weight was assigned to each hospital inpatient stay reported by a key, in-scope person who responded to MEPS for the full period of time that he or she was in scope during 1996. A key person either was a member of an NHIS household at the time of the NHIS interview, or became a member of such a household after being out-of-scope at the time of the 1995 NHIS (examples of the latter situation include newborns and persons returning from military service, an institution, or living outside the United States). A person is in scope whenever he or she is a member of the civilian noninstitutionalized portion of the U.S. population.

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3.1 Details on Person Weights Construction

The person-level weight WTDPER96 was developed using the MEPS Round 1 person-level weight as a base weight (for key, in scope respondents who joined an RU after Round 1, the Round 1 RU weight served as a base weight). The weighting process included an adjustment for nonresponse over Round 2 and the 1996 portion of Round 3, as well as poststratification to population control figures for December 1996 (these figures were derived by scaling the population totals obtained from the March 1997 Current Population Survey (CPS) to reflect the Census Bureau estimated population distribution across age and sex categories as of December, 1996). Variables used in the establishment of person-level poststratification control figures included: poverty status (below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, black but non-Hispanic, and other); sex; and age. Overall, the weighted population estimate for the civilian non-institutionalized population for December 31, 1996 is 265,439,511 persons. The inclusion of key, in scope persons who were not in scope on December 31,1996 brings the estimated total number of persons represented by the MEPS respondents over the course of the year up to 268,905,490 (WTDPER96 > 0). The weighting process included poststratification to population totals obtained from the 1996 Medicare Current Beneficiary Survey (MCBS) for the number of deaths among Medicare beneficiaries in 1996, and poststratification to population totals obtained from the 1996 MEPS Nursing Home Component for the number of individuals admitted to nursing homes.

The MEPS Round 1 weights incorporated the following components: the original household probability of selection for the NHIS; ratio-adjustment to NHIS national population estimates at the household (occupied dwelling unit) level; adjustment for nonresponse at the dwelling unit level for Round 1; and poststratification to figures at the family- and person-level obtained from the March 1996 CPS database.

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4.0 Strategies for Estimation

This file is constructed for efficient estimation of utilization, expenditure, and sources of payment for hospital inpatient care and to allow for estimates of number of persons with inpatient hospital utilization for 1996 (defined as discharges in 1996).

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4.1 Variables with Missing Values

It is essential that the analyst examine all variables for the presence of negative values used to represent missing values. For example, a record with a value of -8 for the first ICD9 condition code (IPICD1X) indicates that the condition was reported as unknown.

For continuous or discrete variables, where means or totals may be taken, it may be necessary to set minus values to values appropriate to the analytic needs. That is, the analyst should either impute a value or set the value to one that will be interpreted as missing by the computing language used. For categorical and dichotomous variables, the analyst may want to consider whether to recode or impute a value for cases with negative values or whether to exclude or include such cases in the numerator and/or denominator when calculating proportions.

Methodologies used for the editing/imputation of expenditure variables (e.g. sources of payment flat fee, mom/baby, hospital/er, and zero expenditures) are described in Section 2.5.6.2.

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4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment

W hile the examples described below illustrate the use of event level data in constructing person level total expenditures, these estimates can also be derived from the person level expenditure file, unless the characteristic of interest is event specific.

In order to produce national estimates related to inpatient hospital utilization, expenditure and sources of payment, the value in each record contributing to the estimates must be multiplied by the weight (WTDPER96) contained on that record.

Example 1:

For example, the total number of hospital inpatient stays, regardless of the length of the hospital stay for the civilian non-institutionalized population of the U.S. in 1996, is estimated as the sum of the weight (WTDPER96) across all records. That is,

Sum of Wj= 26,526,275                                   (1)

Various estimates can be produced based on specific variables and subsets of records.

Example 2:

For example, the estimate for the mean out-of-pocket payment at the hospital inpatient stay level, for hospital inpatient stays with expenditures should be calculated as the weighted mean of the facility bill and doctor's bill paid by self/family. That is,

X bar =(Sum of WjXj) / (Sum of Wj)= $163.46              (2)

where Xj = IPFSF96Xj + IPDSF96Xj and Sum of Wj=25,915,821

for all records with IPEXP96Xj > 0 .

This gives $163.46 as the estimated mean amount of out-of-pocket payment of expenditures associated with hospital inpatient stays (discharges) and 25,915,821 as an estimate of the total number of hospital inpatient stays with expenditure. Both of these estimates are for the civilian non-institutionalized population of the U.S. in 1996.

Example 3:

Another example would be to estimate the mean proportion of total expenditures paid by private insurance for hospital inpatient stays with expenditures. This should be calculated as the weighted mean of proportion of total expenditures paid by private insurance at the stay level. That is,

Y bar =(Sum of WjYj) / (Sum of Wj)=0.4133 where Sum of Wj=25,915,821                 (3)

where Yi=(IPFPV96Xi + IPDPV96Xi) / IPEX96Xi for all records with IPEXP96Xj > 0.

This gives 0.4113 as the estimated mean proportion of total expenditures paid by private insurance for hospital inpatient stays (discharges) with expenditures for the civilian non-institutionalized population of the U.S. in 1996.

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4.3 Estimates of the Number of Persons with Hospital Inpatient Stays

When calculating an estimate of the total number of persons with hospital inpatient stays, users can use a person-level file (MEPS HC-011: Person Level Expenditures and Utilization) or the Hospital Inpatient Stays file. The Hospital Inpatient Stays file must be used, when the measure of interest is defined at the event level. For example, to estimate the number of persons, in the civilian non-institutionalized population of the U.S., discharged from a hospital in1996 with at least one hospital stay of 10 or more nights, this file must be used. This would be estimated as,

Sum of Wixi across all unique persons i on this file,                            (4)

where

Wi is the sampling weight(WTDPER96) for person i

and

Xi = 1 if NUMNIGHX GE 10 for any stay of person i

= 0 otherwise.

Prior to estimation users will need to take into consideration the 32 records with a missing value for NUMNIGHX.

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4.4 Person-Based Ratio Estimates

4.4.1 Person-Based Ratio Estimates Relative to Persons with Hospital Inpatient Use

This file may be used to derive person-based ratio estimates. However, when calculating ratio estimates where the denominator is persons, care should be taken to properly define the unit of analysis as person level. For example, the mean expense for persons with hospital inpatient stays is estimated as,

(Sum of WiZi) / (Sum of Wi) across all unique persons i on this file,              (5)

where

Wi is the sampling weight(WTDPER96) for person i

and

Zi = Sum of IPEXP96Xj across all stays for person i.

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4.4.2 Person-Based Ratio Estimates Relative to the Entire Population

If the ratio relates to the entire population, this file cannot be used to calculate the denominator, as only those persons with at least one hospital inpatient stay are represented on this data file. In this case MEPS HC-011, which has data for all sampled persons, must be used to estimate the total number of persons (i.e. those with use and those without use). For example, the proportion of civilian non-institutionalized population of the U.S. with at least one hospital inpatient stay of four or more days would be estimated as:

(Sum of WiZi) / (Sum of Wi) across all unique persons i on the person level file,       (6)

where

Wi is the sampling weight(WTDPER96) for person i

and

Zi = 1 if NUMNIGHX j GE 4 for any stay of person i on the inpatient stay-level file

= 0 otherwise for all remaining persons on the MEPS HC-011 file.

Prior to estimation users will need to take into consideration the 32 records with a missing value for NUMNIGHX.

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4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data with the Hospital Inpatient Stays Data File

There have been several previous releases of MEPS Household Survey public use data. Unless a variable name common to several tapes is provided, the sampling weights contained on these data files are file-specific. The file-specific weights reflect minor adjustments to eligibility and response indicators due to birth, death, or institutionalization among respondents.

In general, for estimates from a MEPS data file that do not require merging with variables from other MEPS data files, the sampling weight(s) provided on that data file are the appropriate weight(s). When merging a MEPS Household data file to another, the major analytical variable (i.e. the dependent variable) determines the correct sampling weight to use.

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4.6 Variance Estimation

To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, one needs to take into account the complex sample design of MEPS. Various approaches can be used to develop such estimates of variance including use of the Taylor series or various replication methodologies. Replicate weights have not been developed for the MEPS 1996 data. Variables needed to implement a Taylor series estimation approach are described in the paragraph below.

Using a Taylor Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the MEPS full year utilization database are VARSTR96 and VARPSU96, respectively. Specifying a "with replacement" design in a computer software package such as SUDAAN (Shah, 1996) should provide standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), there are over 100 degrees of freedom associated with the corresponding estimates of variance. The following illustrates these concepts using two examples from Section 4.2.

Example 2 from Section 4.2

Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the computer software package SUDAAN will yield an estimate of standard error of $18.96 for the estimated mean of out-of-pocket payment.

Example 3 from section 4.2

Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the computer software package SUDAAN will yield an estimate of standard error of 0.0159 for the weighted mean proportion of total expenditures paid by private insurance.

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5.0 Merging/Linking MEPS Data Files

Data from this file can be used alone or in conjunction with other files. This section provides instructions for linking the hospital stays files with other MEPS public use files, including: the conditions file, the prescribed medicines file, and a person-level file.

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5.1 Linking a Person-Level File to the Hospital Inpatient Stays File

Merging characteristics of interest from person-level files (e.g., HC-008: 1996 Population Characteristics and Utilization Data, or HC-011: 1996 Use and Expenditure File) expands the scope of potential estimates. For example, to estimate the total number of hospital inpatient stays for persons with specific characteristics (e.g., age, race, and sex), population characteristics from a person-level file need to be merged onto the hospital inpatient stays file. This procedure is illustrated below. The Appendix File (HC-010I) provides additional detail on how to merge MEPS data files.

1. Create data set PERS by sorting the the person-level file, HC003, by the person identifier, DUPERSID. Keep only variables to be merged on to the hospital inpatient stays file and DUPERSID.

2. Create data set STAZ by sorting the hospital inpatient stays file by person identifier, DUPERSID.

3. Create final data set NEWSTAZ by merging these two files by DUPERSID, keeping only records on the hospital inpatient stays file.

The following is an example of SAS code which completes these steps:

PROC SORT DATA=HC003(KEEP=DUPERSID AGE SEX EDUC) OUT=PERSX;
BY DUPERSID;
RUN;

PROC SORT DATA=STAYS;
BY DUPERSID;
RUN;

DATA NEWSTAYS;
MERGE STAYS (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;

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5.2 Linking the Hospital Inpatient Stays (HC-010D) to the Medical Conditions File (HC-006) and/or the Prescribed Medicines File (HC-010A)

Due to survey design issues, there are limitations/caveats that an analyst must keep in mind when linking the different files. Those limitations/caveats are listed below. For detailed linking examples, including SAS code, analysts should refer to the Appendix File.

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5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File)

The RXLK file provides a link from the MEPS event files to the prescribed medicine records on HC-010A. When using RXLK, analysts should keep in mind that one hospital inpatient stay can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one hospital inpatient stay or different types of events. When this occurs, it is up to the analyst to determine how the prescribed medicine expenditures should be allocated among those medical events.


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5.4 Limitations/Caveats of CLNK (the Medical Conditions Link File)

The CLNK provides a link from MEPS event files to the Medical Conditions File (HC-006). When using the CLNK, analysts should keep in mind that (1) conditions are self-reported and (2) there may be multiple conditions associated with a hospital inpatient stay. Users should also note that not all hospital inpatient stays link to the condition file.

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6.0 Programming Information

The following are the technical specifications for the HC-010D data files, which are provided in ASCII and SAS formats.

ASCII versions:

File Name: HC10CF1.DAT

Number of Observations: 2,207

Number of Variables: 86

Record Length: 389

Record Format: fixed

Record Identifier and Sort Key: EVNTIDX

File Name: HC10CF2.DAT

Number of Observations: 2,207

Number of Variables: 30

Record Length: 220

Record Format: fixed

Record Identifier and Sort Key: EVNTIDX

SAS Transport versions:

File Name: HC10CF1.SSP

SAS Name: HC10CF1

Number of Observations: 2,207

Number of Variables: 86

Record Identifier and Sort Key: EVNTIDX

File Name: HC10CF2.SSP

SAS Name: HC10CF2

Number of Observations: 2,207

Number of Variables: 30

Record Identifier and Sort Key: EVNTIDX

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References

Cohen, S.B. (1998). Sample Design of the 1996 Medical Expenditure Panel Survey Medical Provider Component. Journal of Economic and Social Measurement. Vol 24, 25-53.

Cohen, S.B. (1997). Sample Design of the 1996 Medical Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS Methodology Report, No. 2. AHCPR Pub. No. 97-0027.

Cohen, J.W. (1997). Design and Methods of the Medical Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS Methodology Report, No. 1. AHCPR Pub. No. 97-0026.

Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology in the Public Service.

Cox, B.G. and Cohen, S.B. (1985). Chapter 8: Imputation Procedures to Compensate for Missing Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York.

Health Care Financing Administration (1980). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public Health Services.

Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy. (1999). Jossey-Bass Inc, San Francisco.

Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC: Research Triangle Institute.

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Attachment 1

Definitions

Dwelling Units, Reporting Units, Families, and Persons ­ The definitions of Dwelling Units (DUs) and Group Quarters in the MEPS Household Survey are generally consistent with the definitions employed for the National Health Interview Survey. The dwelling unit ID (DUID) is a five-digit random ID number assigned after the case was sampled for MEPS. The person number (PID) uniquely identifies all persons within the dwelling unit. The variable DUPERSID is the combination of the variables DUID and PID.

A Reporting Unit (RU) is a person or group of persons in the sampled dwelling unit who are related by blood, marriage, adoption or other family association, and who are to be interviewed as a group in MEPS. Thus, the RU serves chiefly as a family-based "survey operations" unit rather than an analytic unit. Regardless of the legal status of their association, two persons living together as a "family" unit were treated as a single reporting unit if they chose to be so identified.

Unmarried college students under 24 years of age who usually live in the sampled household, but were living away from home and going to school at the time of the Round 1 MEPS interview, were treated as a Reporting Unit separate from that of their parents for the purpose of data collection. These variables can be found on MEPS person level files.

In-Scope ­ A person was classified as in-scope (INSCOPE) if he or she was a member of the U.S. civilian, non-institutionalized population at some time during the Round 1 interview. This variable can be found on MEPS person level files.

Keyness ­The term "keyness" is related to an individual's chance of being included in MEPS. A person is key if that person is appropriately linked to the set of 1995 NHIS sampled households designated for inclusion in MEPS. Specifically, a key person either was a member of an NHIS household at the time of the NHIS interview, or became a member of such a household after being out-of-scope prior to joining that household (examples of the latter situation include newborns and persons returning from military service, an institution, or living outside the United States).

A non-key person is one whose chance of selection for the NHIS (and MEPS) was associated with a household eligible but not sampled for the NHIS, who happened to have become a member of a MEPS reporting unit by the time of the MEPS Round 1 interview. MEPS data, (e.g., utilization and income) were collected for the period of time a non-key person was part of the sampled unit to permit family level analyses. However, non-key persons who leave a sample household would not be recontacted for subsequent interviews. Non-key individuals are not part of the target sample used to obtain person level national estimates.

It should be pointed out that a person may be key even though not part of the civilian, non-institutionalized portion of the U.S population. For example, a person in the military may be living with his or her civilian spouse and children in a household sampled for the 1995 NHIS. The person in the military would be considered a key person for MEPS. However, such a person would not receive a person-level sample weight so long as he or she was in the military. All key persons who participated in the first round of the 1996 MEPS received a person level sample weight except those who were in the military. The variable indicating "keyness" is KEYNESS. This variable can be found on MEPS person level files.

Eligibility ­The eligibility of a person for MEPS pertains to whether or not data were to be collected for that person. All key, in-scope persons of a sampled RU were eligible for data collection. The only non-key persons eligible for data collection were those who happened to be living in the same RU as one or more key persons, and their eligibility continued only for the time that they were living with a key person. The only out-of-scope persons eligible for data collection were those who were living with key in-scope persons, again only for the time they were living with a key person. Only military persons meet this description. A person was considered eligible if they were eligible at any time during Round 1. The variable indicating "eligibility" is ELIGRND1, where 1 is coded for persons eligible for data collection for at least a portion of the Round 1 reference period, and 2 is coded for persons not eligible for data collection at any time during the first round reference period. This variable can be found on MEPS person level files.

Pre-imputed - This means that only a series of logical edits were applied to the HC data to correct for several problems including outliers, copayments or charges reported as total payments, and reimbursed amounts counted as out of pocket payments. Missing data remains.

Unimputed - This means that only a series of logical edits were applied to the MPC data to correct for several problems including outliers, copayments or charges reported as total payments, and reimbursed amounts counted as out of pocket payments. This data was used as the imputation source to account for missing HC data.

Imputation -Imputation is more often used for item missing data adjustment through the use of predictive models for the missing data, based on data available on the same (or similar) cases. Hot-deck imputation creates a data set with complete data for all nonrespondent cases, often by substituting the data from a respondent case that resembles the nonrespondent on certain known variables.

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D. Codebooks (link to separate file)

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E. Variable-Source Crosswalk
MEPS HC010D: 1996 HOSPITAL INPATIENT STAYS
File 1:

Survey Administration and ID Variables

Variable

Description

Source

DUID

Dwelling unit ID

Assigned in sampling

PID

Person number

Assigned in sampling

DUPERSID

Sample person ID (DUID + PID)

Assigned in sampling

EVENTIDX

Event ID : DUPERSID + EVENTN

Assigned in Sampling

EVENTRN

Round number

CAPI derived

ERHEVIDX

Emergency Room/Hospital Stay Link

Constructed

FFID11X

Flat fee ID

CAPI derived

MPCDATA

MPC Data Indicator

Constructed

MBLINK

Mother/Baby expenditure Flag

Constructed

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Characteristics of Hospital Inpatient Stay

Variable

Description

Source

IPBEGYR

Event start date – year

CAPI derived

IPBEGMM

Event start date – month

CAPI derived

IPBEGDD

Event start date – day

CAPI derived

IPENDYR

Event end date – year

CAPI derived

IPENDMM

Event end date – month

CAPI derived

IPENDDD

Event end date – day

CAPI derived

NUMNIGHX

Number of nights stayed at Hospital - Edited

(Edited/imputed)

NUMNIGHT

Number of nights stayed at Hospital

HS01

EMERROOM

Did stay begin with emergency room visit

HS02

SPECCOND

Hospital stay related to condition

HS03

RSNINHOS

Reason entered hospital

HS05

ANYOPER

Any operations or surgery performed

HS06

SURGPROC

Main surgical procedure

HS07

VAPLACE

Hospital is a VA facility

Constructed

IPICD1X

3 digit ICD-9 condition code

HS02 (Edited)

IPICD2X

3 digit ICD-9 condition code

HS02 (Edited)

IPICD3X

3 digit ICD-9 condition code

HS02 (Edited)

IPICD4X

3 digit ICD-9 condition code

HS02 (Edited)

IPPRO1X

2 digit ICD-9 procedure code

HS02 (Edited)

IPPRO2X

2 digit ICD-9 procedure code

HS02 (Edited)

IPCCC1X

Modified Clinical Classification Code

Constructed/Edited

IPCCC2X

Modified Clinical Classification Code

Constructed/Edited

IPCCC3X

Modified Clinical Classification Code

Constructed/Edited

IPCCC4X

Modified Clinical Classification Code

Constructed/Edited

NUMCOND

Total number of COND records linked to this event

Constructed

DSCHPMED

Medicines prescribed at discharge

HS08

DROUTSID

Any of the DRS seen outside the hospital

HS10

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Flat Fee Variables

Variable

Description

Source

FFIPTYPX

Stem or Leaf of Flat Fee Group

FF01, FF02

FFIP96

# Hospital stays in flat fee in 1996

FF02

FFTOT96

Total # visits (pure/mixed) in flat fee for 1996

FF02 (edited)?

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Imputed Total Expenditure Variables

Variable

Description

Source

IPEXP96X

Total expenditure for hospital inpatient stay

Constructed

IPTCH96X

Total charge for hospital inpatient stay

Constructed

 

Imputed Facility Expenditure Variables

Variable

Description

Source

IPFSF96X

Facility amount paid, family (imputed)

Imputed

IPFMR96X

Facility amount paid, Medicare (imputed)

Imputed

IPFMD96X

Facility amount paid, Medicaid (imputed)

Imputed

IPFPV96X

Facility amount paid, private insurance (imputed)

Imputed

IPFVA96X

Facility amount paid, Veterans (imputed)

Imputed

IPFCH96X

Facility amount paid, CHAMP/CHAMPVA (imputed)

Imputed

IPFOF96X

Facility amount paid, other federal (imputed)

Imputed

IPFSL96X

Facility amount paid, state/local govt. (imputed)

Imputed

IPFWC96X

Facility amount paid, Worker’s Comp (imputed)

Imputed

IPFOR96X

Facility amount paid, other private (imputed)

Imputed

IPFOU96X

Facility amount paid, other public (imputed)

Imputed

IPFOT96X

Facility amount paid, other insurance (imputed)

Imputed

IPFXP96X

Facility sum of payments IPFSF96X – IPFOT96X

Constructed

IPFTC96X

Facility total charge (imputed)

Imputed

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Imputed Separately Billing Physician Expenditure Variables

Variable

Description

Source

IPDSF96X

Doctor amount paid, family (imputed)

Imputed

IPDMR96X

Doctor amount paid, Medicare (imputed)

Imputed

IPDMD96X

Doctor amount paid, Medicaid (imputed)

Imputed

IPDPV96X

Doctor amount paid, private insurance (imputed)

Imputed

IPDVA96X

Doctor amount paid, Veterans (imputed)

Imputed

IPDCH96X

Doctor amount paid, CHAMP/CHAMPVA (imputed)

Imputed

IPDOF96X

Doctor amount paid, other federal (imputed)

Imputed

IPDSL96X

Doctor amount paid, state/local govt. (imputed)

Imputed

IPDWC96X

Doctor amount paid, Worker’s Comp (imputed)

Imputed

IPDOR96X

Doctor amount paid, other private (imputed)

Imputed

IPDOU96X

Doctor amount paid, other public (imputed)

Imputed

IPDOT96X

Doctor amount paid, other insurance (imputed)

Imputed

IPDXP96X

Doctor sum of payments IPPSF96X – IPPOT96X

Constructed

IPDTC96X

Doctor total charge (imputed)

Imputed

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Imputation Flag Variables

Variable

Description

Source

IMPIPFSF

Imputation flag for IPFSF96X

Constructed

IMPIPFMR

Imputation flag for IPFMR96X

Constructed

IMPIPFMD

Imputation flag for IPFMD96X

Constructed

IMPIPFPV

Imputation flag for IPFPV96X

Constructed

IMPIPFVA

Imputation flag for IPFVA96X

Constructed

IMPIPFCH

Imputation flag for IPFCH96X

Constructed

IMPIPFOF

Imputation flag for IPFOF96X

Constructed

IMPIPFSL

Imputation flag for IPFSL96X

Constructed

IMPIPFWC

Imputation flag for IPFWC96X

Constructed

IMPIPFOR

Imputation flag for IPFOR96X

Constructed

IMPIPFOU

Imputation flag for IPFOU96X

Constructed

IMPIPFOT

Imputation flag for IPFOT96X

Constructed

IMPIPCHG

Imputation flag for IPFTC96X

Constructed

IMPIPNUM

Number of separately billing physicians associated with hospital stay

Constructed

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Weights

Variable

Description

Source

WTDPER96

Poverty/Mortality Adjusted Person weight, 1996

Constructed

VARSTR96

Variance estimation stratum, 1996

Constructed

VARPSU96

Variance estimation PSU, 1996

Constructed

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File 2:Pre-imputed/Unimputed Expenditure Variables

Variable

Description

Source

DUID

Dwelling unit ID

Assigned in sampling

PID

Person number

Assigned in sampling

DUPERSID

Sample person ID (DUID + PID)

Assigned in sampling

EVENTIDX

Event ID : DUPERSID + EVENTN

Assigned in Sampling

HHSFFIDX

Household reported flat fee identifier (unedited)

CAPI generated

IPSF96H

Household reported amount paid, family (pre-imputed)

CP07, CP09, CP11-CP340V2

IPMR96H

Household reported amount paid, Medicare (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPMD96H

Household reported amount paid, Medicaid (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPPV96H

Household reported amount paid, private insurance (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPVA96H

Household reported amount paid, Veterans (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPCH96H

Household reported amount paid, CHAMP/CHAMPVA (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPOF96H

Household reported amount paid, other federal (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPSL96H

Household reported amount paid, state/local govt. (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPWC96H

Household reported amount paid, Worker’s Comp (pre-imputed)

CP07, CP09, CP11-CP34 OV2

IPOT96H

Household reported amount paid, other insurance. (pre-imputed)

CP07, CP09, CP11-CP34OV2

IPTC96H

Household reported total charge (pre-imputed)

CP09A, CP09OV

IPSF96M

MPC reported amount paid, family (unimputed)

8, 9(a.), 12(a-d), 18, 19(a.), 22(a-d)

IPMR96M

MPC reported amount paid, Medicare (unimputed)

8, 9(b.), 12(a-d), 18, 19(b.), 22(a-d)

IPMD96M

MPC reported amount paid, Medicaid (unimputed)

8, 9(c.), 12(a-d), 18, 19(c.), 22(a-d)

IPPV96M

MPC reported amount paid, private insurance (unimputed)

8, 9(d.), 12(a-d), 18, 19(d.), 22(a-d)

IPVA96M

MPC reported amount paid, Veterans (unimputed)

8, 9(e.), 12(a-d), 18, 19(e.), 22(a-d)

IPCH96M

MPC reported amount paid, CHAMP/CHAMPVA (unimputed)

8, 9(f.), 12(a-d), 18, 19(f.), 22(a-d)

IPOF96M

MPC reported amount paid, other federal (unimputed)

8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d)

IPSL96M

MPC reported amount paid, state/local govt. (unimputed)

8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d)

IPWC96M

MPC reported amount paid, Worker’s Comp (unimputed)

8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d)

IPOT96M

MPC reported amount paid, other insurance (unimputed)

8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d)

IPTC96M

MPC reported total charge (unimputed)

7, 17(a, b), 18

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Weights

Variable

Description

Source

WTDPER96

Poverty/Mortality Adjusted Person weight, 1996

Constructed

VARSTR96

Variance estimation stratum, 1996

Constructed

VARPSU96

Variance estimation PSU, 1996

Constructed

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